Forest cover dynamics analysis and prediction modeling using logistic regression model
•The change in forest cover was predicted using logistic regression model (LRM).•Explanatory variables (EV) associated with forest conversion process were analyzed.•Distance from forest edge, roads, settlements and slope position classes were the EV.•Highest regression coefficient (β=−26.892) was ob...
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| Veröffentlicht in: | Ecological indicators 2014-10, Vol.45, p.444-455 |
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| creator | Kumar, Rakesh Nandy, S. Agarwal, Reshu Kushwaha, S.P.S. |
| description | •The change in forest cover was predicted using logistic regression model (LRM).•Explanatory variables (EV) associated with forest conversion process were analyzed.•Distance from forest edge, roads, settlements and slope position classes were the EV.•Highest regression coefficient (β=−26.892) was observed for distance from forest edge.•The LRM modeled forest conversion by predicting forest cover with high accuracy (ROC=87%).
Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β=−26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2km2 of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC=87%). |
| doi_str_mv | 10.1016/j.ecolind.2014.05.003 |
| format | Article |
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Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β=−26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2km2 of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC=87%).</description><identifier>ISSN: 1470-160X</identifier><identifier>EISSN: 1872-7034</identifier><identifier>DOI: 10.1016/j.ecolind.2014.05.003</identifier><language>eng</language><publisher>Amsterdam: Elsevier Ltd</publisher><subject>Animal and plant ecology ; Animal, plant and microbial ecology ; Applied ecology ; Biological and medical sciences ; Conservation, protection and management of environment and wildlife ; Conversion ; Dependent variable ; Dynamic tests ; Dynamics ; Explanatory variables ; Forest cover dynamics ; Forestry ; Forests ; Fundamental and applied biological sciences. Psychology ; General aspects ; General aspects. Techniques ; General forest ecology ; Generalities. Production, biomass. Quality of wood and forest products. General forest ecology ; Logistic regression model ; Logistics ; Mathematical models ; Methods and techniques (sampling, tagging, trapping, modelling...) ; Parks, reserves, wildlife conservation. Endangered species: population survey and restocking ; Prediction ; Regression ; Regression analysis ; Synecology</subject><ispartof>Ecological indicators, 2014-10, Vol.45, p.444-455</ispartof><rights>2014 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c471t-7a4c98a1d1a4dc2abc5f6560818cd12b0204b4fa4915f5a50de5d5091cfbfe703</citedby><cites>FETCH-LOGICAL-c471t-7a4c98a1d1a4dc2abc5f6560818cd12b0204b4fa4915f5a50de5d5091cfbfe703</cites><orcidid>0000-0003-4127-4035</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S1470160X14002052$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28664051$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Kumar, Rakesh</creatorcontrib><creatorcontrib>Nandy, S.</creatorcontrib><creatorcontrib>Agarwal, Reshu</creatorcontrib><creatorcontrib>Kushwaha, S.P.S.</creatorcontrib><title>Forest cover dynamics analysis and prediction modeling using logistic regression model</title><title>Ecological indicators</title><description>•The change in forest cover was predicted using logistic regression model (LRM).•Explanatory variables (EV) associated with forest conversion process were analyzed.•Distance from forest edge, roads, settlements and slope position classes were the EV.•Highest regression coefficient (β=−26.892) was observed for distance from forest edge.•The LRM modeled forest conversion by predicting forest cover with high accuracy (ROC=87%).
Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β=−26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2km2 of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC=87%).</description><subject>Animal and plant ecology</subject><subject>Animal, plant and microbial ecology</subject><subject>Applied ecology</subject><subject>Biological and medical sciences</subject><subject>Conservation, protection and management of environment and wildlife</subject><subject>Conversion</subject><subject>Dependent variable</subject><subject>Dynamic tests</subject><subject>Dynamics</subject><subject>Explanatory variables</subject><subject>Forest cover dynamics</subject><subject>Forestry</subject><subject>Forests</subject><subject>Fundamental and applied biological sciences. Psychology</subject><subject>General aspects</subject><subject>General aspects. Techniques</subject><subject>General forest ecology</subject><subject>Generalities. Production, biomass. Quality of wood and forest products. General forest ecology</subject><subject>Logistic regression model</subject><subject>Logistics</subject><subject>Mathematical models</subject><subject>Methods and techniques (sampling, tagging, trapping, modelling...)</subject><subject>Parks, reserves, wildlife conservation. Endangered species: population survey and restocking</subject><subject>Prediction</subject><subject>Regression</subject><subject>Regression analysis</subject><subject>Synecology</subject><issn>1470-160X</issn><issn>1872-7034</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNqNkMtKAzEUhoMoWKuPIMxGcDPjSZrMZSVSrAoFNyruQuYkU1JmJjWZFvr2ZmjpVjfJWXz_uXyE3FLIKND8YZ0ZdK3tdcaA8gxEBjA7IxNaFiwtYMbPY80LSGkO35fkKoQ1xFxV5RPytXDehCFBtzM-0ftedRZDonrV7oMdC51svNEWB-v6pHPaxEmrZBvGt3UrGwaLiTer2CackGty0ag2mJvjPyWfi-eP-Wu6fH95mz8tU-QFHdJCcaxKRTVVXCNTNYomFzmUtERNWQ0MeM0bxSsqGqEEaCO0gIpiUzcmnjYl94e-G-9-tvEQ2dmApm1Vb9w2SJpzxgoOQvwDZUUlSjYrIyoOKHoXgjeN3HjbKb-XFOSoXK7lUbkclUsQMiqPubvjCBVQtY1XPdpwCrMyz-MqNHKPB85ENTtrvAxoTY9Rszc4SO3sH5N-AURsmvQ</recordid><startdate>20141001</startdate><enddate>20141001</enddate><creator>Kumar, Rakesh</creator><creator>Nandy, S.</creator><creator>Agarwal, Reshu</creator><creator>Kushwaha, S.P.S.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>SOI</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><orcidid>https://orcid.org/0000-0003-4127-4035</orcidid></search><sort><creationdate>20141001</creationdate><title>Forest cover dynamics analysis and prediction modeling using logistic regression model</title><author>Kumar, Rakesh ; Nandy, S. ; Agarwal, Reshu ; Kushwaha, S.P.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c471t-7a4c98a1d1a4dc2abc5f6560818cd12b0204b4fa4915f5a50de5d5091cfbfe703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Animal and plant ecology</topic><topic>Animal, plant and microbial ecology</topic><topic>Applied ecology</topic><topic>Biological and medical sciences</topic><topic>Conservation, protection and management of environment and wildlife</topic><topic>Conversion</topic><topic>Dependent variable</topic><topic>Dynamic tests</topic><topic>Dynamics</topic><topic>Explanatory variables</topic><topic>Forest cover dynamics</topic><topic>Forestry</topic><topic>Forests</topic><topic>Fundamental and applied biological sciences. Psychology</topic><topic>General aspects</topic><topic>General aspects. Techniques</topic><topic>General forest ecology</topic><topic>Generalities. Production, biomass. Quality of wood and forest products. General forest ecology</topic><topic>Logistic regression model</topic><topic>Logistics</topic><topic>Mathematical models</topic><topic>Methods and techniques (sampling, tagging, trapping, modelling...)</topic><topic>Parks, reserves, wildlife conservation. Endangered species: population survey and restocking</topic><topic>Prediction</topic><topic>Regression</topic><topic>Regression analysis</topic><topic>Synecology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kumar, Rakesh</creatorcontrib><creatorcontrib>Nandy, S.</creatorcontrib><creatorcontrib>Agarwal, Reshu</creatorcontrib><creatorcontrib>Kushwaha, S.P.S.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Ecological indicators</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kumar, Rakesh</au><au>Nandy, S.</au><au>Agarwal, Reshu</au><au>Kushwaha, S.P.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forest cover dynamics analysis and prediction modeling using logistic regression model</atitle><jtitle>Ecological indicators</jtitle><date>2014-10-01</date><risdate>2014</risdate><volume>45</volume><spage>444</spage><epage>455</epage><pages>444-455</pages><issn>1470-160X</issn><eissn>1872-7034</eissn><abstract>•The change in forest cover was predicted using logistic regression model (LRM).•Explanatory variables (EV) associated with forest conversion process were analyzed.•Distance from forest edge, roads, settlements and slope position classes were the EV.•Highest regression coefficient (β=−26.892) was observed for distance from forest edge.•The LRM modeled forest conversion by predicting forest cover with high accuracy (ROC=87%).
Forest cover conversion and depletion are of global concern due to their role in global warming. The present study attempted to study the forest cover dynamics and prediction modeling in Bhanupratappur Forest Division of Kanker district in Chhattisgarh province of India. The study aims to examine and analyze the various explanatory variables associated with forest conversion process and predict forest cover change using logistic regression model (LRM). The forest cover for the periods 1990 and 2000, derived from Landsat TM satellite imagery, was used to predict the forest cover for 2010. The predictive performance of the model was assessed by comparing the model-predicted forest cover with the actual forest cover for 2010. To explain the effects of anthropogenic pressure on forest, this study considered three distance variables viz., distance from forest edge, roads and settlements, and slope position classes as explanatory variables of forest change. The highest regression coefficient (β=−26.892) was noticed in case of distance from forest edge, which signifies the higher probability of forest change in areas that are closer to the forest edges. The analysis showed that forest cover has undergone continuous change between 1990 and 2010, leading to the loss of 107.2km2 of forest area. The LRM successfully predicted the forest cover for the period 2010 with reasonably high accuracy (ROC=87%).</abstract><cop>Amsterdam</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.ecolind.2014.05.003</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-4127-4035</orcidid></addata></record> |
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| subjects | Animal and plant ecology Animal, plant and microbial ecology Applied ecology Biological and medical sciences Conservation, protection and management of environment and wildlife Conversion Dependent variable Dynamic tests Dynamics Explanatory variables Forest cover dynamics Forestry Forests Fundamental and applied biological sciences. Psychology General aspects General aspects. Techniques General forest ecology Generalities. Production, biomass. Quality of wood and forest products. General forest ecology Logistic regression model Logistics Mathematical models Methods and techniques (sampling, tagging, trapping, modelling...) Parks, reserves, wildlife conservation. Endangered species: population survey and restocking Prediction Regression Regression analysis Synecology |
| title | Forest cover dynamics analysis and prediction modeling using logistic regression model |
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